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Estimate betas using various regression methods

Source: R/fmri_betas.R
estimate_betas.fmri_dataset.Rd

This function estimates betas (regression coefficients) for fixed and random effects using various regression methods including mixed models, least squares, and PLS.

Usage

# S3 method for class 'fmri_dataset'
estimate_betas(
  x,
  fixed = NULL,
  ran,
  block,
  method = c("mixed", "mixed_cpp", "lss", "lss_naive", "lss_cpp", "pls", "pls_global",
    "ols"),
  basemod = NULL,
  maxit = 1000,
  fracs = 0.5,
  progress = TRUE,
  ...
)

Arguments

x

An object of class fmri_dataset representing the fMRI dataset.

fixed

A formula specifying the fixed regressors that model constant effects (i.e., non-varying over trials).

ran

A formula specifying the random (trialwise) regressors that model single trial effects.

block

A formula specifying the block factor.

method

The regression method for estimating trialwise betas; one of "mixed", "mixed_cpp", "lss", "lss_naive", "lss_cpp", "pls", "pls_global", or "ols".

basemod

A baseline_model instance to regress out of data before beta estimation (default: NULL).

maxit

Maximum number of iterations for optimization methods (default: 1000).

fracs

Fraction of voxels used for prewhitening.

progress

Logical; show progress bar.

...

Additional arguments passed to the estimation method.

Value

A list of class "fmri_betas" containing the following components:

  • betas_fixed: NeuroVec object representing the fixed effect betas.

  • betas_ran: NeuroVec object representing the random effect betas.

  • design_ran: Design matrix for random effects.

  • design_fixed: Design matrix for fixed effects.

  • design_base: Design matrix for baseline model.

  • basemod: Baseline model object.

  • fixed_model: Fixed effect model object.

  • ran_model: Random effect model object.

  • estimated_hrf: The estimated HRF vector (NULL for most methods).

See also

fmri_dataset, baseline_model, event_model

Examples

if (FALSE) { # \dontrun{
facedes <- read.table(system.file("extdata", "face_design.txt", package = "fmrireg"), header=TRUE)
facedes$frun <- factor(facedes$run)
scans <- paste0("rscan0", 1:6, ".nii")

dset <- fmri_dataset(scans=scans, mask="mask.nii", TR=1.5, 
        run_length=rep(436,6), event_table=facedes)
fixed = onset ~ hrf(run)
ran = onset ~ trialwise()
block = ~ run

betas <- estimate_betas(dset, fixed=fixed, ran=ran, block=block, method="mixed")
} # }